Identification and Quantification of Autoantibodies against Prostate-Specific Antigens by Immunoaffinity-Mass Spectrometry
Bibliographic record
Abstract
ABSTRACT Prostate-specific antigen (PSA) utilized clinically to diagnose prostate cancer (PCa) has limitations of low diagnostic specificity and lack of prognostic information. Novel PCa markers are needed to improve PCa diagnostics. Here, we hypothesized that some prostate-specific antigens leaking into systemic circulation during prostate tissue transformation and PCa progression could trigger production of autoantibodies, and that these autoantibodies could improve PCa diagnostics. Autoantibodies against prostate-specific antigens were previously exclusively detected by serological immunoassays, but the existence of autoantibodies was often debated due to potential cross-reactivity and non-specific binding of indirect immunoassays. Here, we aimed at developing a proteome-wide platform for serological assays that could discover and quantify antigen-specific autoantibodies in blood serum and evaluate their diagnostic potential. We developed targeted and shotgun Immunoaffinity-Mass Spectrometry (IA-MS) assays to quantify serum autoantibodies against prostate-specific proteins PSA (kallikrein-3; KLK3_HUMAN), kallikrein-4 (KLK4_HUMAN), prostate-specific membrane antigen (FOLH1_HUMAN), and prostatic acid phosphatase (PPAP_HUMAN). IA-SRM assays resolved false positive identifications, discovered IgG1, IgA1, and IgM as the most prevalent isotypes of autoantibodies, and provided reproducible quantification of autoantibodies in negative biopsy, low-risk PCa, and metastatic PCa serum samples. Anti-KLK3 and anti-KLK4 IgG1 autoantibodies were detected in 75% (median concentration 4 ng/mL) and 67% (median 11 ng/mL) of PCa serum samples, respectively. Indirect immunoassays collectively detecting IgG autoantibodies, a mixture of four subclasses, revealed poor signal-to-noise ratios, false positives, and a surprisingly high number of false negatives, debating the usefulness of indirect immunoassays for discovery and quantification of autoantibodies. The presented proteome-wide serology assays will facilitate the quantification of PCa autoantibodies, paving the way to improved diagnostics of PCa and comprehensive evaluation of immune response to prostate-specific antigens.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".